# Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number

Source: [Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number](<https://www.sota2.com/research/paper/68fa7b111e209131cea867f8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-06T05:01:36.493Z

Authors: Jingyuan Zhou, Hao Qian, Shikui Tu, Lei Xu

Published: 2025-09-01T14:06:58.000Z

## Abstract

Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent generative models have shown great potential, they suffer from unstable probability dynamics and mismatch between generated molecule size and the protein pockets geometry, resulting in inconsistent quality and off-target effects. We propose PAFlow, a novel target-aware molecular generation model featuring prior interaction guidance and a learnable atom number predictor. PAFlow adopts the efficient flow matching framework to model the generation process and constructs a new form of conditional flow matching for discrete atom types. A protein-ligand interaction predictor is incorporated to guide the vector field toward higher-affinity regions during generation, while an atom number predictor based on protein pocket information is designed to better align generated molecule size with target geometry. Extensive experiments on the CrossDocked2020 benchmark show that PAFlow achieves a new state-of-the-art in binding affinity (up to -8.31 Avg. Vina Score), simultaneously maintains favorable molecular properties.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multi-objective Drug Discovery on BRD4\_HUMAN](<https://www.sota2.com/research/sota/multi-objective-drug-discovery-on-brd4_human.md>): Multi-objective Drug Discovery; BRD4\_HUMAN; BA: 8.33.

- [Multi-objective Drug Discovery on ACAA1 THIK\_HUMAN](<https://www.sota2.com/research/sota/multi-objective-drug-discovery-on-acaa1-thik_human.md>): Multi-objective Drug Discovery; ACAA1 THIK\_HUMAN; Binding Affinity: 7.9.

- [Multi-objective Drug Discovery on c-MET MET\_HUMAN](<https://www.sota2.com/research/sota/multi-objective-drug-discovery-on-c-met-met_human.md>): Multi-objective Drug Discovery; c-MET MET\_HUMAN; Binding Affinity (BA): 10.63.

- [Multi-objective Drug Discovery on Aggregate c-MET, BRD4, ACAA1](<https://www.sota2.com/research/sota/multi-objective-drug-discovery-on-aggregate-c-met-brd4-acaa1.md>): Multi-objective Drug Discovery; Aggregate c-MET, BRD4, ACAA1; Average Rank: 7.56.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.01486>)

[PDF](<https://arxiv.org/pdf/2509.01486.pdf>)